Abstract Advancements in geostationary satellites allow monitoring of terrestrial photosynthesis at sub-daily scales, offering unprecedented ...

We estimate an empirical model of consumption disasters using a new panel data set on personal consumer expenditure for 24 countries and more than 100 years, and study its implications for asset ...

The depth estimation model is trained on 500,000 synthetic images generated from 600 high-resolution photogrammetry human scans, ensuring high accuracy for monocular depth estimation. Empirical ...

The package includes routine functions for univariate analyses multiple threshold selection diagnostics, optimization, bias-correction and tangent exponential model approximations, non-parametric ...

BayesFrag is an open-source Python library to perform Bayesian parameter estimation of empirical seismic fragility models. The methodology is presented in Bodenmann L ...

In this paper, the limitations of the NRTL model to correlate LLE data sets for island-type ternary systems are discussed. The lack of flexibility and the uncertainty in the equilibrium solution are ...

Empirical models can be used to represent the recrystallization process in frozen food as ... growth during recrystallization in frozen Tilapia samples and had the advantage of being simple and robust ...

The purpose of this paper is to propose a highly effective Pólya-Gamma Gibbs sampling algorithm (Polson et al., 2013) based on auxiliary variables to estimate the deterministic inputs, noisy “and” ...

Abstract: Model of a plant is used to get an insight of the physical system behavior and there always a scope exists to improve the model parameters by using proper estimation techniques. First ...